The Study of GRNN for Wind Speed Forecasting Based on Markov Chain

In view of modeling accuracy problems of General Regression Neural Network (GRNN), the improved GRNN has been used to forecast wind speed. Firstly, K-fold cross validation was used to select smooth parameter of GRNN, and the influence of K value was analyzed. Then, Markov Chain (MC) was introduced to correct the results of GRNN to improve the accuracy of GRNN. And C-average clustering algorithm was used to state division, the number of state and correction results were emphatically discussed. Finally, the improved GRNN was applied to wind speed forecasting by use of the actual data gathered from a wind farm in Shanxi province to further test the proposed method. The experimental results show that the superiority of the proposed method compared with GRNN.